Prof Julia Boettcher

This course is compulsory on the BSc in Data Science and BSc in Mathematics with Data Science. This course is available on the BSc in Mathematics and Economics, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students.

Students must have completed Mathematical Proof and Analysis (MA102) or Introduction to Abstract Mathematics (MA103).

Basic Knowledge of Python is essential, such as provided by ST101 or a pre-sessional provided by the Digital Skills Lab.

Introduction to the fundamental principles of data structures and algorithms and their efficient implementation. Developing algorithmic thinking. Basic toolkit for the design and analysis of algorithms: Running time, recurrence relations, big-O notation, amortised analysis, correctness, finite induction, loop invariants. Tour of the most important data structures, fundamental algorithms, and algorithm design techniques: lists, stacks, queues, dynamic arrays, hash tables, heaps, priority queues, disjoint set unions, binary search trees, incremental and recursive algorithms, divide-and-conquer, greedy algorithms, randomisation in algorithms, sorting algorithms, algorithmic lower bounds, graph algorithms.

This course is delivered through a combination of lectures and classes totalling a minimum of 30 hours across Winter Term.

Written answers to set problems will be expected on a weekly basis.

• T H Cormen, C E Leiserson, R L Rivest & C Stein, Introduction to Algorithms, MIT Press, 3rd edition, 2009.

Exam (80%, duration: 2 hours) in the spring exam period.
Coursework (20%) in the WT.